POGO: A Method for individualization of Automated Insulin Delivery Systems

Over the past few years, automated Insulin Delivery (AID) systems have rapidly revolutionized the management of type 1 diabetes (T1D). As the vast majority of first and second generation AID systems are based on mathematical models, it can be difficult to account for the large variability in insulin needs between patients. Therefore, many AID systems are optimized for an average patient, with limited adaptation to individual insulin requirements. We propose a simple wrapper function, POGO, to adjust an AID system to the insulin needs of any user, by rescaling of the insulin inputs and the controller output linearly based on a ratio of insulin needs. We then demonstrate improvement, in silico, in model prediction and glycemic control performances across a wide range of insulin needs while avoiding the need for complex personalization.